An image defrosting technique for temperature-differential automatic assembly method of hole-shaft interference fit structures

In the automatic assembly of temperature-differential method for the hole-shaft interference fit structure, due to the huge temperature difference between the low temperature and the room temperature environment, the surface of the hole-shaft parts is prone to the formation of a frost layer, which seriously affects the visual measurement accuracy of the assembly pose. To address this problem, this paper draws on the research idea of image dehazing and proposes a Single Image Edge-Enhanced Defrosting network (SIEED) based on the coding-decoding structure to realize efficient defrosting from the image level. SIEED comprises the following key modules: the Edge-Enhanced Convolution Module (EECM), which leverages the sensitivity of convolution operators to edge features, enhancing edge information extraction; the Spatial-Guided Attention Module (SGAM), which employs local sensing techniques to address the non-uniform frost distribution through regional differentiation; the Weight-Based Iterative Fusion Module (WIFM), which dynamically fuses shallow and deep features to mitigate the loss of low-frequency features induced by deep convolution; and the Adversarial Discrimination Module (ADM), which incorporates global and local discriminators to balance the realism of localized defrosting with the overall coherence, using adversarial generation to produce defrosting images closer to reality. In addition, this paper proposes an automated acquisition method for the real dataset of hole-shaft images to guarantee the reliability and practicality of model training. The experimental results show that SIEED exhibits excellent performance in the frost-covered image defrosting task, and the pose measurements of its reconstructed images are highly close to those of the clean images, which fully verifies the validity, and reliability of the method in practical applications.